Synthetic Data

Exploring the Synthetic Personality Data: Introverts vs Extroverts Dataset

Team Syncora

August 1, 2025

Studying personality, especially introversion vs. extroversion, is one of the important aspects of psychology, behavioral science, marketing, and AI.

But here’s a challenge: getting large, privacy-safe datasets is tough. That’s where synthetic data can help.

In this blog, we dive into a synthetic personality dataset on GitHub that mimics the behavior of introverts and extroverts. This introverts vs extroverts dataset is perfect for researchers, data scientists, and AI teams.

We’ll also show how to create synthetic data for training psychology AI models.

Let’s see in detail.

What is the Synthetic Personality Dataset About?

The synthetic personality dataset is a collection of artificially generated data designed to mimic the behavioral and social patterns associated with different personality types.

Since synthetic datasets do not contain any personal information, they are privacy-safe. These datasets let you:

Explore personality traits

Model behavior

Train machine learning algorithms

We’ve created a dataset that contains 10,000 high-fidelity synthetic records generated by an advanced synthetic data generation tool. It mirrors real-world behavioral distributions while ensuring that no real individuals are represented. This makes it both ethically sound and privacy-safe.

Where to get this Introvert vs Extrovert Dataset?

For anyone interested in personality prediction or behavioral modeling, the full dataset is publicly available on GitHub. It can integrate easily with your analytical or machine learning workflow

Explore and download on GitHub below.

Key Behavioral Features Included

This synthetic data for psychology research has a broad set of relevant variables that reflect daily life and social interactions linked to personality types. It includes:

Time_spent_Alone: Average daily hours spent alone, ranging from 0 to 11.

Stage_fear: Binary indicator of stage fright (0 for no, 1 for yes).

Social_event_attendance: Number of social events attended weekly (0–10).

Going_outside: Frequency of outdoor activities per week (0–7).

Drained_after_socializing: Social exhaustion indicator (0 or 1).

Friends_circle_size: Number of close friends (0–15).

Post_frequency: Weekly social media posts count (0–10).

Personality: Target label with 0 representing extroverts and 1 representing introverts.

This dataset offers a holistic perspective on social and behavioral tendencies associated with introversion and extroversion. It is suitable for a variety of AI modeling and research tasks.

Dataset Characteristics and Format

Encoding: Binary encoding is used for categorical traits.

Size: 10,000 records across 8 variables that reflect balanced representation of introverts and extroverts (no bias).

Format: Ready-to-use CSV files compatible with Python, R, Excel, and more.

Missing Data: Intentionally included in select features to support imputation practice and realistic data preprocessing scenarios.

This dataset has a balanced mix of introverts and extroverts, which helps machine learning models avoid bias and make more accurate and reliable predictions.

Applications of This Dataset in Psychology Research and AI

This synthetic personality dataset has a wide range of use cases in psychology, data science, and AI development:

Personality Prediction Models: Train and test machine learning algorithms to classify personality types.

Behavioral Trend Analysis: Study how habits such as social event attendance or social media activity differ across personality traits.

Data Preprocessing Practice: Utilize missing data for experience with imputation, encoding, and feature engineering.

Visualization & EDA Projects: Create insightful dashboards and plots to explore personality-linked behavioral patterns.

Bias-Free AI Training: Build privacy-safe AI models that comply with data protection regulations while preserving predictive utility.

Researchers working on human-computer interaction (HCI), marketing audience segmentation, and social science behavioral studies will find this dataset useful as a foundation for experimentation and prototyping.

How to Generate Synthetic Personality Data in 2025?

You can create personality datasets in two ways:

A) Manual Method:

Start with real data (if available)

Define features (e.g., social activity, communication style) and structure the dataset.

Generate synthetic samples using rules, statistics, or use models like GANs.

Validate and test for accuracy and balance.

B) Using Synthetic Data Generation Platform

Just upload raw data into Synocra.ai’s platform

AI agents clean, structure, and synthesize synthetic data in minutes.

Download ready-to-use & privacy-compliant personality dataset.

FAQs

1.What behavioral traits does the synthetic introvert vs extrovert dataset include?

The dataset has traits such as time spent alone, social event attendance, stage fright, social exhaustion, outdoor activity frequency, social media post frequency, and size of close friend circles.

2.How can synthetic data help in psychology and AI research?

Synthetic data provides a scalable, ethical way to study personality and social behaviors. It is used to train machine learning models, practice data preprocessing, and conduct behavioral trend analysis. All this can be done without privacy constraints or data scarcity issues.

To Sum it Up

Synthetic personality datasets offer a powerful, privacy-safe way to study human behavior at scale. Whether you’re exploring introversion and extroversion, training AI models, or conducting psychological research, synthetic data removes the usual barriers of access and ethics. The dataset we explored mirrors real behavioral patterns without compromising privacy, making it ideal for researchers, data scientists, and developers alike. With tools like Syncora.ai, generating such data is faster and easier than ever. Now’s the time to build smarter models with better data.